Attitude towards Online Shopping during Pandemics: Do Gender, Social Factors and Platform Quality Matter?
Bibliographic record
Abstract
Because of the advancement of electronic commerce, online shopping has emerged, merging commercial and social activities and enhancing the social presence and value of the online environment. To improve the understanding of the changes in the consumer behavior during the COVID-19 pandemic, this study proposes a set of characteristics connected to the social side of online shopping and their influence on client purchasing attitude in addition to the quality of the platforms that are being used (service quality, system quality and information quality). For this matter, a survey of 289 Lebanese people was circulated in 2021 and a quantitative method was used to answer three research questions. Types of goods purchased and frequency of buying on-line were tested to check the presence of any gender differences, in addition to the relationship between the variables studied in the model. According to the research, social presence, social value, and tendency to compare products on different shopping platforms all have a significant correlation with the attitude towards online shopping, where the system quality was the least significant. When it comes to purchasing frequency and product types, the data gathered imply that gender disparities are considerable. This study does not consider the consumer’s living environment or whether there are any age differences between the generations shopping online.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".